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One of the most important applications of information technology is to summarize data and predict new data based on existing values. For example, in stock market analysis, many investors use technical analysis tools to create a model that helps them in decision making. To minimize the uncertainties of the stock market, investors implement prediction models modified with their opinions. An ETF, which has a strong mutual connectivity between different portfolios, gets attention of the public by its low risk, intraday tradability and tax efficiency. In this paper, we propose a model in which investors’ opinion can be applied via Partially Observable Markov Decision Processes (POMDP), so that investors can intervene in the model to improve the prediction and make greater profit. Since an ETF has a strong mutual connectivity, we also use historical data to find out the relative changes between the chosen portfolios. This helps the model work better in POMDP structure.

A Robust Time Series Prediction Model Using POMDP and Data Analysis